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                                <h2 id="&#x8BFB;&#x8BBA;&#x6587;&#x7CFB;&#x5217;&#xFF1A;deep-transfer-learning-person-re-identification">&#x8BFB;&#x8BBA;&#x6587;&#x7CFB;&#x5217;&#xFF1A;Deep transfer learning person re-identification</h2>
<p><a href="https://arxiv.org/abs/1611.05244" target="_blank">arxiv 2016</a> by Mengyue Geng, Yaowei Wang, Tao Xiang, Yonghong Tian</p>
<h3 id="transfer-learning">Transfer Learning</h3>
<p>&#x65E7;&#x6570;&#x636E;&#x8BAD;&#x7EC3;&#x5F97;&#x5230;&#x7684;&#x5206;&#x7C7B;&#x5668;&#xFF0C;&#x5728;&#x65B0;&#x7684;&#x6570;&#x636E;&#x4E0A;&#x91CD;&#x65B0;&#x8BAD;&#x7EC3;&#xFF0C;&#x4ECE;&#x800C;&#x5728;&#x65B0;&#x6570;&#x636E;&#x4E0A;&#x53D6;&#x5F97;&#x6BD4;&#x8F83;&#x597D;&#x7684;&#x8868;&#x73B0;&#xFF0C;&#x65B0;&#x6570;&#x636E;&#x4E0E;&#x65E7;&#x6570;&#x636E;&#x6709;&#x76F8;&#x4F3C;&#x7684;&#x5730;&#x65B9;&#xFF0C;&#x4F46;&#x5177;&#x6709;&#x4E0D;&#x540C;&#x7684;&#x5206;&#x5E03;&#x3002;</p>
<p><img src="transfer_fig1.png" alt=""></p>
<h3 id="fine-tuning&#x4E00;&#x822C;&#x6B65;&#x9AA4;">Fine tuning&#x4E00;&#x822C;&#x6B65;&#x9AA4;</h3>
<p>&#x8FD9;&#x662F;InceptionV4&#x7684;&#x56FE;&#x793A;</p>
<p><img src="transfer_fig2.png" alt=""></p>
<ul>
<li>&#x79FB;&#x9664;Softmax&#x5206;&#x7C7B;&#x5C42;</li>
<li>&#x6362;&#x6210;&#x4E0E;&#x76EE;&#x6807;&#x6570;&#x636E;&#x96C6;&#x8F93;&#x51FA;&#x7EF4;&#x6570;&#x76F8;&#x540C;&#x7684;Softmax&#x5C42;</li>
<li>&#x51BB;&#x7ED3;&#x9760;&#x8FD1;&#x8F93;&#x5165;&#x7684;&#x5377;&#x79EF;&#x5C42;</li>
<li>&#x4EE5;&#x8F83;&#x9AD8;&#x7684;&#x5B66;&#x4E60;&#x7387;&#x8BAD;&#x7EC3;&#x5206;&#x7C7B;&#x5C42;</li>
<li>&#x4EE5;&#x5F88;&#x4F4E;&#x7684;&#x5B66;&#x4E60;&#x7387;&#x5FAE;&#x8C03;&#x5269;&#x4E0B;&#x7684;&#x5377;&#x79EF;&#x5C42;</li>
</ul>
<h3 id="&#x8BBA;&#x6587;&#x6838;&#x5FC3;&#x6A21;&#x578B;">&#x8BBA;&#x6587;&#x6838;&#x5FC3;&#x6A21;&#x578B;</h3>
<p><img src="transfer_fig3.png" alt=""></p>
<p>&#x51E0;&#x4E2A;&#x521B;&#x65B0;&#x70B9;&#xFF1A;</p>
<ul>
<li>&#x5BF9;&#x4E8E;CNN&#x8F93;&#x51FA;&#x7684;&#x4E24;&#x5F20;&#x56FE;&#x7684;&#x7279;&#x5F81;&#xFF0C;&#x4F7F;&#x7528;&#x4E86;&#x76F8;&#x540C;&#x7684;dropout&#x800C;&#x975E;&#x5404;&#x81EA;&#x72EC;&#x7ACB;&#x7684;&#x968F;&#x673A;dropout</li>
<li>&#x4F7F;&#x7528;&#x4E86;&#x4E8C;&#x5206;&#x7C7B;&#x52A0;&#x591A;&#x5206;&#x7C7B;&#x4E24;&#x79CD;loss&#xFF0C;&#x4E8C;&#x5206;&#x7C7B;&#x7528;&#x4E8E;&#x5224;&#x65AD;&#x4E24;&#x5F20;&#x56FE;&#x4E2D;&#x7684;&#x4EBA;&#x662F;&#x5426;&#x76F8;&#x540C;&#xFF0C;&#x591A;&#x5206;&#x7C7B;&#x7528;&#x4E8E;&#x63CF;&#x8FF0;&#x4E24;&#x5F20;&#x56FE;&#x4E2D;&#x5404;&#x81EA;&#x7684;&#x4EBA;&#x7269;ID</li>
<li>&#x5206;&#x4E24;&#x9636;&#x6BB5;&#x8FDB;&#x884C;Fine tune&#xFF0C;&#x5148;&#x5FAE;&#x8C03;&#x591A;&#x5206;&#x7C7B;&#xFF0C;&#x518D;&#x8054;&#x5408;&#x4E8C;&#x5206;&#x7C7B;&#x548C;&#x591A;&#x5206;&#x7C7B;&#x8FDB;&#x884C;&#x5FAE;&#x8C03;&#xFF0C;&#x907F;&#x514D;&#x591A;&#x5206;&#x7C7B;&#x7F51;&#x7EDC;&#x4E0D;&#x7A33;&#x5B9A;&#x5BF9;&#x4E8C;&#x5206;&#x7C7B;&#x7684;&#x5F71;&#x54CD;</li>
</ul>
<h3 id="unsupervised-transfer-learning">Unsupervised Transfer Learning</h3>
<p><img src="transfer_fig4.png" alt=""></p>
<h4 id="self-training">Self-training</h4>
<ul>
<li>&#x5C06;&#x56FE;&#x7247;&#x5747;&#x5206;&#x4E3A;&#x4E24;&#x7EC4;&#xFF08;&#x8BBA;&#x6587;&#x4E2D;&#x662F;&#x6309;&#x6444;&#x50CF;&#x5934;&#x5212;&#x5206;&#x7684;&#xFF09;</li>
<li>&#x5C06;B&#x7EC4;&#x4E2D;&#x7684;&#x6BCF;&#x5F20;&#x56FE;&#x7247;&#xFF0C;&#x4E0E;A&#x7EC4;&#x4E2D;CNN&#x8F93;&#x51FA;&#x76F8;&#x4F3C;&#x5EA6;&#x6700;&#x9AD8;&#x7684;&#x56FE;&#x7247;&#x5F52;&#x4E3A;&#x4E00;&#x7C7B;&#xFF0C;&#x4ECE;&#x800C;&#x6784;&#x9020;&#x51FA;&#x591A;&#x5206;&#x7C7B;&#x6807;&#x7B7E;</li>
<li>&#x5582;&#x5165;CNN&#x8BAD;&#x7EC3;</li>
<li>&#x8FED;&#x4EE3;&#x591A;&#x6B21;</li>
</ul>
<h4 id="co-training">Co-training</h4>
<ul>
<li>&#x7531;&#x4E8E;CNN&#x8F93;&#x51FA;&#x7684;&#x56FE;&#x7247;&#x76F8;&#x4F3C;&#x5EA6;&#x4E0D;&#x4E00;&#x5B9A;&#x771F;&#x7684;&#x53EF;&#x9760;&#xFF0C;&#x5B58;&#x5728;&#x566A;&#x97F3;&#xFF0C;&#x56E0;&#x6B64;Self-training&#x6548;&#x679C;&#x6CA1;&#x6709;&#x7279;&#x522B;&#x597D;</li>
<li>&#x5BFB;&#x627E;&#x4E00;&#x4E2A;&#x4E92;&#x8865;&#x7684;&#x6A21;&#x578B;&#xFF0C;&#x5C06;&#x7279;&#x5F81;&#x6620;&#x5C04;&#x5230;&#x53E6;&#x4E00;&#x4E2A;&#x5B50;&#x7A7A;&#x95F4;&#x4E2D;</li>
<li>&#x5C06;B&#x7EC4;&#x4E2D;&#x7684;&#x6BCF;&#x5F20;&#x56FE;&#x7247;&#xFF0C;&#x4E0E;A&#x7EC4;&#x4E2D;&#x5B50;&#x7A7A;&#x95F4;&#x76F8;&#x4F3C;&#x5EA6;&#x6700;&#x9AD8;&#x7684;&#x56FE;&#x7247;&#x5F52;&#x4E3A;&#x4E00;&#x7C7B;&#xFF0C;&#x6784;&#x9020;&#x591A;&#x5206;&#x7C7B;&#x6807;&#x7B7E;</li>
<li>&#x5582;&#x5165;CNN&#x8BAD;&#x7EC3;</li>
<li>&#x8FED;&#x4EE3;&#x591A;&#x6B21;</li>
</ul>
<h4 id="co-model">Co-Model</h4>
<ul>
<li>CNN&#x8BA1;&#x7B97;&#x5F97;&#x5230;&#x6DF1;&#x5EA6;&#x7279;&#x5F81;&#xFF1A; <script type="math/tex; ">𝑦=\theta(𝑥)</script></li>
<li>Learn a subspace defined by a dictionary D and a new representation Z in the subspace.</li>
<li><script type="math/tex; ">(D^*, Z^*) = min_{D,Z} ||Y-DZ||_F^2 + \lambda\Omega(Z) s.t. ||d_i||_2^2 \leq 1 </script></li>
<li>&#x5176;&#x4E2D;<script type="math/tex; ">||Y-DZ||_F^2</script>&#x662F;reconstruction error</li>
<li><script type="math/tex; ">\Omega(Z) = \sum_{i,j} W_{ij}||z_i - z_j||_2^2</script><ul>
<li>&#x5F53;<script type="math/tex; ">y_i</script>&#x548C;<script type="math/tex; ">y_j</script>&#x662F;&#x6700;&#x8FD1;&#x90BB;&#x65F6;&#xFF0C;$W_{ij}$&#x4E3A;1&#xFF0C;&#x5426;&#x5219;&#x4E3A;0</li>
<li>&#x4ECE;&#x800C;&#x6700;&#x5C0F;&#x5316;&#x6700;&#x8FD1;&#x90BB;&#x7684;representation z&#x7684;&#x5DEE;&#x5F02;</li>
</ul>
</li>
</ul>
<h3 id="trick-result">Trick Result</h3>
<ul>
<li>Softmax loss VS Multi loss&#xFF1A; 76.6% -&gt; 83.7%&#xFF08;Market1501&#xFF09;</li>
<li>&#x4E00;&#x81F4;&#x7684;Dropout VS &#x968F;&#x673A;&#x7684;Dropout&#xFF1A; 80.8%-&gt; 83.7% &#xFF08;Market1501&#xFF09;</li>
<li>Two-stepped VS one-stepped: 47.6%-&gt;56.3%(VIPeR)</li>
</ul>
<h3 id="supervised-transfer-learning-result">Supervised Transfer Learning Result</h3>
<table>
<thead>
<tr>
<th style="text-align:center">DataSet</th>
<th style="text-align:center">State of the art</th>
<th style="text-align:center">Transfer</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">CUHK03</td>
<td style="text-align:center">75.3</td>
<td style="text-align:center">85.4</td>
</tr>
<tr>
<td style="text-align:center">Market1501</td>
<td style="text-align:center">82.21</td>
<td style="text-align:center">83.7</td>
</tr>
<tr>
<td style="text-align:center">VIPeR</td>
<td style="text-align:center">53.5</td>
<td style="text-align:center">56.3</td>
</tr>
<tr>
<td style="text-align:center">PRID</td>
<td style="text-align:center">40.9</td>
<td style="text-align:center">43.6</td>
</tr>
<tr>
<td style="text-align:center">CUHK01</td>
<td style="text-align:center">86.6</td>
<td style="text-align:center">93.2</td>
</tr>
</tbody>
</table>
<p>(&#x8868;&#x4E2D;&#x90FD;&#x662F;top1&#x51C6;&#x786E;&#x7387;)</p>
<h3 id="unsupervised-transfer-learning-result">Unsupervised Transfer Learning Result</h3>
<table>
<thead>
<tr>
<th style="text-align:center">DataSet</th>
<th style="text-align:center">State of the art</th>
<th style="text-align:center">Transfer</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">VIPeR</td>
<td style="text-align:center">33.5</td>
<td style="text-align:center">45.1</td>
</tr>
<tr>
<td style="text-align:center">PRID</td>
<td style="text-align:center">25.0</td>
<td style="text-align:center">36.2</td>
</tr>
<tr>
<td style="text-align:center">CUHK01</td>
<td style="text-align:center">41.0</td>
<td style="text-align:center">68.8</td>
</tr>
</tbody>
</table>
<h3 id="compare-with-other-unsupervised-method">Compare with other unsupervised method</h3>
<p>&#x4F7F;&#x7528;&#x5176;&#x4ED6;&#x65E0;&#x76D1;&#x7763;&#x65B9;&#x6CD5;&#x8FDB;&#x884C;&#x5B9E;&#x9A8C;&#x5BF9;&#x6BD4;</p>
<table>
<thead>
<tr>
<th style="text-align:center">Method</th>
<th style="text-align:center">Top1 acc</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Self-training</td>
<td style="text-align:center">42.8</td>
</tr>
<tr>
<td style="text-align:center">SubSpace</td>
<td style="text-align:center">42.3</td>
</tr>
<tr>
<td style="text-align:center">Transfer</td>
<td style="text-align:center">45.1</td>
</tr>
<tr>
<td style="text-align:center">CNN+AutoEncoder</td>
<td style="text-align:center">36.4</td>
</tr>
<tr>
<td style="text-align:center">Adversarial</td>
<td style="text-align:center">22.8</td>
</tr>
</tbody>
</table>
<p>&#x5176;&#x4E2D;SubSpace&#x4E3A;&#x53EA;&#x4F7F;&#x7528;Co-Model&#xFF0C;&#x4E0D;&#x4F7F;&#x7528;CNN&#x6A21;&#x578B;&#xFF0C;Self-training&#x4E3A;&#x53EA;&#x4F7F;&#x7528;CNN&#x6A21;&#x578B;&#xFF0C;Transfer&#x662F;&#x4E24;&#x8005;&#x7ED3;&#x5408;&#x7684;Co-training&#x3002;</p>
<p>&#x603B;&#x4F53;&#x6765;&#x8BF4;&#x8FD9;&#x79CD;&#x65E0;&#x76D1;&#x7763;&#x7684;&#x65B9;&#x6CD5;&#x53D6;&#x5F97;&#x4E86;&#x6BD4;&#x8F83;&#x597D;&#x7684;&#x6548;&#x679C;&#xFF0C;&#x5728;&#x5C0F;&#x6570;&#x636E;&#x96C6;&#x4E0A;&#x751A;&#x81F3;&#x8D85;&#x8FC7;&#x4E86;&#x6709;&#x76D1;&#x7763;&#x7684;&#x6548;&#x679C;&#x3002;</p>
<p>&#x5982;&#x679C;&#x89C9;&#x5F97;&#x6211;&#x7684;&#x6587;&#x7AE0;&#x5BF9;&#x4F60;&#x6709;&#x5E2E;&#x52A9;&#xFF0C;&#x53EF;&#x4EE5;&#x524D;&#x5F80;github&#x70B9;&#x4E2A;<a href="https://github.com/ahangchen/windy-afternoon" target="_blank">star</a></p>

                                
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